Meteorological parameters which are most significant for ozone forecasting were chosen in the multiple regression analysis for the daily time series. Then correlations between the variables we~e investigated, both for the daily and temporary values. There was confirmed a strong relationship between atmospheric conditions and ozone concentrations as well as autocorrelations of the temporary time series of ozone from different monitoring stations. Diversification of autocorrelation values arises probably from different receptor locations which was confirmed by the principal component analysis. There were also shown dependences between the ozone time series from different monitoring stations. Strong space-time relationships of ozone concentrations and meteorological conditions in the Black Triangle region can be used in modeling and forecasting of ozone episodes.